Figure 2 - Spatial Panels

Introduction

In this Rmarkdown document we are going to create the spatial pannels necessary for Figure 2.

To do this we will load the MAGIC-denoised data to better visualize genes and ease with the annotation when using specific marker genes. The MAGIC denoised data was generated in the script spatial_transcriptomics/CD4-analysis/MAGIC_denoising.Rmd.

Libraries

library(Seurat)
library(ggpubr)
library(cowplot)
library(dplyr)
library(ggplot2)
library(SPATA2)

Setting parameters

Loading necessary paths and parameters

set.seed(123)
source(here::here("misc/paths.R"))
source(here::here("utils/bin.R"))

"{fig2}/{plt_dir}" %>%
  glue::glue() %>%
  here::here() %>%
  dir.create(path = .,
             showWarnings = FALSE,
             recursive = TRUE)

"{fig2}/{robj_dir}" %>%
  glue::glue() %>%
  here::here() %>%
  dir.create(path = .,
             showWarnings = FALSE,
             recursive = TRUE)

Load data

The data used in this Rmarkdown document comes from 03-clustering_integration.Rmd where the data was integrated.

merged_se <- "misc/{robj_dir}/20220215_tonsil_atlas_spatial_seurat_obj.rds" %>%
  glue::glue() %>%
  here::here() %>%
  readRDS(file = .)

# Load SPOTlight data
spotlight_ls <- "{cd4}/{robj_dir}/spotlight_ls_cd4_new.rds" %>%
  glue::glue() %>% 
  here::here() %>%
  readRDS(file = .)

# Load SPOTlight data
nm_df <- "{cd4}/{robj_dir}/cd4_nm_df.rds" %>%
  glue::glue() %>% 
  here::here() %>%
  readRDS(file = .)

Preprocess data

decon_mtrx <- spotlight_ls[[2]]
decon_mtrx <- decon_mtrx[, colnames(decon_mtrx) != "res_ss"]

# Set as 0 cell types predicted to be under 3 % of the spot
decon_mtrx[decon_mtrx < 0.03] <- 0

Change column names

new_cn <- data.frame(mod_nm = colnames(decon_mtrx)) %>%
  dplyr::left_join(nm_df, by = "mod_nm") %>%
  dplyr::mutate(plt_nm = dplyr::if_else(is.na(plt_nm), mod_nm, plt_nm)) %>%
  dplyr::distinct() %>%
  dplyr::pull(plt_nm)

colnames(decon_mtrx) <- new_cn

We are going to add the deconvolution to the Seurat object.

merged_se@meta.data <- cbind(merged_se@meta.data, decon_mtrx)

Subset sample of interest

sample_id <- "esvq52_nluss5"
sp_sub <- merged_se[, merged_se@meta.data$gem_id == sample_id]
sp_sub@images <- sp_sub@images[Seurat::Images(sp_sub) == sample_id]

Panels

Cell types interest

Look at the location of each cell type in each slice separately

# Iterate over cell types
ct_int <- c("Naive", "GC-Tfh-0X40", "GC-Tfh-SAP", "Tfh-LZ-GC")

nm_donor <- id_sp_df %>% dplyr::filter(gem_id == sample_id) %>% dplyr::pull(donor_id)
# Iterate over cell types
ct_plt_ls <- lapply(ct_int, function(i) {
  tmp_plt <- Seurat::SpatialFeaturePlot(
    object = merged_se,
    features = i,
    alpha = c(0, 1),
    images = sample_id,
    image.alpha = 1) +
    ggplot2::scale_fill_gradientn(
      colors = heat.colors(10, rev = TRUE)) +
    ggplot2::scale_alpha(range = c(0, 1)) +
    ggplot2::labs(title = stringr::str_wrap(string = i,
                                   width = 25),
         fill = "") +
    ggplot2::theme(
      plot.title = ggplot2::element_text(
        hjust = 0.5,
        size = 20,
        face = "bold"))

  return(tmp_plt)
})

(plt_arr <- cowplot::plot_grid(
  plotlist = ct_plt_ls,
  axis = "trbl",
  align = "hv",
  nrow = 2,
  ncol = 2))

"{fig2}/{plt_dir}/Figure-2i_cd4_deconv_{nm_donor}.pdf" %>%
  glue::glue() %>%
  here::here() %>%
  cowplot::save_plot(
    filename = .,
    plot = plt_arr,
    base_height = 10,
    base_width = 10)

Associated supplementary

Check Topic profiles

nmf_mod_ls <- spotlight_ls[[1]]
nmf_mod <- nmf_mod_ls[[1]]

h <- NMF::coef(nmf_mod)
rownames(h) <- paste("Topic", 1:nrow(h), sep = "_")
topic_profile_plts <- SPOTlight::dot_plot_profiles_fun(
  h = h,
  train_cell_clust = nmf_mod_ls[[2]])

(topic_diag <- topic_profile_plts[[2]] +
  ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 90), 
                 axis.text = ggplot2::element_text(size = 12)))

"{fig2}/{plt_dir}/supplementary_cd4_topic_diagonal.pdf" %>%
  glue::glue() %>%
  here::here() %>%
  cowplot::save_plot(
    filename = .,
    plot = topic_diag,
    base_height = 8,
    base_width = 8)

Look at all cells profiles

(topics_all <- topic_profile_plts[[1]] +
  ggplot2::theme(
    axis.text.y = ggplot2::element_blank(),
    axis.text.x = ggplot2::element_blank(),
    axis.title = ggplot2::element_blank()))

"{fig2}/{plt_dir}/supplementary_cd4_topic_all_cells.pdf" %>%
  glue::glue() %>%
  here::here() %>%
  cowplot::save_plot(
    filename = .,
    plot = topics_all,
    base_height = 20,
    base_width = 20)

All cell types